A model for virtual network embedding using Artificial Bee Colony

Pathak, Isha; Tripathi, Atul; Vidyarthi, Deo Prakash

doi:10.1002/dac.3573

Cited by 5 publications

(3 citation statements)

References 42 publications

(60 reference statements)

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“…By changing solutions and using improvement operators, individual of population can discover several near‐optimal solutions. The ABC algorithm uses three types of bees (employed bees, onlooker bees, and scout bees) to search the space 30 . Each group of bees reinforces the previous solutions, and there is a collaboration between them to find solutions.…”

Section: Related Workmentioning

confidence: 99%

A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

Mohammadzadeh

Gharehchopogh

2020

Int J Communication

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Summary There exist numerous high‐dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high‐dimensional problems. An appropriate approach to solve such problems is to apply multi‐agent systems (MASs) along with the metaheuristic algorithms. The present paper proposes a new approach based on the MASs and the concept of agent, which is named MAS as Metaheuristic (MAMH) method. In the proposed method, several basic and powerful metaheuristic algorithms are considered as separate agents, each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. Altogether, the proposed method was tested on 32 complex benchmark functions, the results of which indicated the effectiveness and powerfulness of the proposed method for solving high‐dimensional optimization problems. In addition, in this paper, the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection. According to the results, the proposed method exhibited a higher degree of precision in the detection of spam emails compared to other metaheuristic algorithms and methods.

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Section: Related Workmentioning

confidence: 99%

A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

Mohammadzadeh

Gharehchopogh

2020

Int J Communication

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“…Some metaheuristic algorithms perform well in solving some problems, such as flowshop scheduling problems [29][30][31][32]. erefore, some researchers use the metaheuristic algorithm [33][34][35] to solve the virtual network mapping algorithm. ese researchers have made useful attempts and achieved some results.…”

Section: Heuristic Solutions Vs Exact Solutions Vs Metaheuristicmentioning

confidence: 99%

EPVNE: An Efficient Parallelizable Virtual Network Embedding Algorithm

Zhang

2019

Wireless Communications and Mobile Computing

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Virtual network embedding (VNE) problem is a key issue in network virtualization technology, and much attention has been paid to the virtual network embedding. However, very little research work focuses on parallelized virtual network embedding problems which assumes that the substrate infrastructure supports parallel computing and allows one virtual node to be mapped to multiple substrate nodes. Based on the work of Liang and Zhang, we extend the well-known VNE to parallelizable virtual network embedding (PVNE) in this paper. Furthermore, to the best of our knowledge, we give the first formulation of the PVNE problem. A new heuristic algorithm named efficient parallelizable virtual network embedding (EPVNE) is proposed to reduce the cost of embedding the VN request and increase the VN request acceptance ratio. EPVNE is a two-stage mapping algorithm, which first performs node mapping and then performs link mapping. In the node mapping phase, we present a simple and efficient virtual node and physical node sorting formula and perform the virtual node mapping in order. When mapping virtual nodes, we map virtual nodes to physical nodes that just meet the CPU requirements. Substrate nodes with more CPU resources will be retained for subsequent virtual network mapping requests. In the link mapping phase, Dijkstra’s algorithm is used to find a substrate path for each virtual link. Finally, simulations are carried out and simulation results show that our algorithm performs better than the existing heuristic algorithms.

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“…The proposed algorithm competed PSO counterpart in all evaluation metrics. Similar to [105], ABC technique was studied in [106] to deal with dynamic VNE problem across multiple ISPs. A penalty function and a scaling factor based search were designed to improve the solution quality and the global convergence.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Distributed and Parallel Metaheuristic-based Algorithms for Online Virtual Resource Allocation

Nguyen¹

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I would like to express my sincere gratitude to my supervisor, Prof. Changcheng Huang, for his support, guidance, and encouragement. It is my privilege to have a great opportunity to work with him. His invaluable advice and patient guidance help me how to conduct a scientific research, and explore new research topics independently. He consistently motivates me to do my best, or encourages me when I have a challenging time during research. His profound supervision pushes me beyond my expectations. I would have never been able to accomplish my dissertation without his tremendous support.During the long journey of a PhD student, it has been a great pleasure to work with faculty, staff, alumni, colleagues and students at Carleton University. I would like to thank Qiao Lu, Steve Drew, Pei Jin, Hieu Le and other colleagues in laboratory for their support, precious advice and stimulating discussions on my work.Finally, I would like to appreciate the unconditional support, patience and encouragement from my wife Trang Pham, my daughter Helen Nguyen, our families, Tung Pham, Tin Vo and other friends throughout my PhD journey.

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A model for virtual network embedding using Artificial Bee Colony

Cited by 5 publications

References 42 publications

A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

EPVNE: An Efficient Parallelizable Virtual Network Embedding Algorithm

Distributed and Parallel Metaheuristic-based Algorithms for Online Virtual Resource Allocation

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